skip to main content
10.1145/3170358.3170385acmotherconferencesArticle/Chapter ViewAbstractPublication PageslakConference Proceedingsconference-collections
short-paper

Investigating learning strategies in a dispositional learning analytics context: the case of worked examples

Published: 07 March 2018 Publication History

Abstract

This study aims to contribute to recent developments in empirical studies on students' learning strategies, whereby the use of trace data is combined with self-report data to distinguish profiles of learning strategy use [3--5]. We do so in the context of an application of dispositional learning analytics in a large introductory course mathematics and statistics, based on blended learning. Building on our previous work which showed marked differences in how students used worked examples as a learning strategy [7, 11], this study compares different profiles of learning strategies with learning approaches, learning outcomes, and learning dispositions. One of our key findings is that deep learners were less dependent on worked examples as a resource for learning, and that students who only sporadically used worked examples achieved higher test scores.

References

[1]
R. Azevedo, J. Harley, G. Trevors, M. Duffy, R. Feyzi-Behnagh, F. Bouchet, et al. 2013. Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agents systems. In International handbook of metacognition and learning technologies, R. Azevedo & V. Aleven (Eds.). Springer, Amsterdam, The Netherlands, 427--449.
[2]
S. Buckingham Shum and R. Deakin Crick. 2012. Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, S. Buckingham Shum, D. Gasevic, and R. Ferguson (Eds.). ACM, New York, NY, USA.
[3]
D. Gašević, J. Jovanović, A. Pardo, and S. Dawson. 2017. Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2017), 113--128.
[4]
D. Gašević, N. Mirriahi, S. Dawson, and S. Joksimović. 2017. Effects of instructional conditions and experience on the adoption of a learning tool. Computers in Human Behavior, 67(2017), 207--220.
[5]
J. Jovanović, D. Gašević, S. Dawson, A. Pardo, and N. Mirriahi. 2017. Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33(2017), 74--85.
[6]
B. M. McLaren, T. van Gog, C. Ganoe, M. Karabinos, and D. Yaron. 2016. The efficiency of worked examples compared to erroneous examples, tutored problem solving, and problem solving in classroom experiments. Computers in Human Behavior, 55(2016), 87--99.
[7]
Q. Nguyen, D. T. Tempelaar, B. Rienties, and B. Giesbers. 2016. What learning analytics based prediction models tell us about feedback preferences of students. In e-Learners and Their Data, Part 1: Conceptual, Research, and Exploratory Perspectives. Quarterly Review of Distance Education, R. Amirault, and Y. Visser (Eds.), 17(2016), 3.
[8]
A. Renkl. 2014. The Worked Examples Principle in Multimedia Learning. In The Cambridge Handbook of Multimedia Learning, R. E. Mayer (Ed.). Cambridge University Press, Cambridge, UK, 391--412.
[9]
B. Rienties, S. Cross, and Z. Zdrahal. 2017. Implementing a Learning Analytics Intervention and Evaluation Framework: What Works? In Big data and learning analytics: Current theory and practice in higher education, B. Kei Daniel (Ed.). Springer International Publishing, Cham, 147--166.
[10]
D. T. Tempelaar, H. Cuypers, E. Van de Vrie, A. Heck, H. Van der Kooij. 2013. Formative Assessment and Learning Analytics. In Proceedings of the 3rd International Conference on Learning Analytics and Knowledge, D. Suthers and K. Verbert (Eds.). ACM New York, 205--209.
[11]
D. T. Tempelaar, J. Mittelmeier, B. Rienties, and Q. Nguyen. 2017. Student profiling in a dispositional learning analytics application using formative assessment, Computers in Human Behavior (in press).
[12]
D. T. Tempelaar, B. Rienties, and B. Giesbers. 2015. In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior, 47(2015), 157--167.
[13]
D.T. Tempelaar, B. Rienties, and Q. Nguyen. 2017. Towards actionable learning analytics using dispositions. IEEE Transactions on Education, 10(2017), 6--16.
[14]
P. H. Winne. 2017. Leveraging big data to help each learner upgrade learning and accelerate learning science. Teachers College Record, 119(2017), 1--24.

Cited By

View all
  • (2023)Multi-Institutional Multi-National Studies of Parsons ProblemsProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633498(57-107)Online publication date: 22-Dec-2023
  • (2022)Chilean University Students’ Digital Learning Technology Usage Patterns and Approaches to LearningECNU Review of Education10.1177/209653112110735385:1(37-64)Online publication date: 8-Feb-2022
  • (2022)Parsons Problems and BeyondProceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3571785.3574127(191-234)Online publication date: 27-Dec-2022
  • Show More Cited By

Index Terms

  1. Investigating learning strategies in a dispositional learning analytics context: the case of worked examples

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Other conferences
          LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge
          March 2018
          489 pages
          ISBN:9781450364003
          DOI:10.1145/3170358
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 07 March 2018

          Permissions

          Request permissions for this article.

          Check for updates

          Author Tags

          1. blended learning
          2. dispositional learning analytics
          3. learning strategies
          4. prediction models
          5. technology enhanced learning

          Qualifiers

          • Short-paper

          Conference

          LAK '18
          LAK '18: International Conference on Learning Analytics and Knowledge
          March 7 - 9, 2018
          New South Wales, Sydney, Australia

          Acceptance Rates

          LAK '18 Paper Acceptance Rate 35 of 115 submissions, 30%;
          Overall Acceptance Rate 236 of 782 submissions, 30%

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • Downloads (Last 12 months)17
          • Downloads (Last 6 weeks)1
          Reflects downloads up to 14 Feb 2025

          Other Metrics

          Citations

          Cited By

          View all
          • (2023)Multi-Institutional Multi-National Studies of Parsons ProblemsProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633498(57-107)Online publication date: 22-Dec-2023
          • (2022)Chilean University Students’ Digital Learning Technology Usage Patterns and Approaches to LearningECNU Review of Education10.1177/209653112110735385:1(37-64)Online publication date: 8-Feb-2022
          • (2022)Parsons Problems and BeyondProceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3571785.3574127(191-234)Online publication date: 27-Dec-2022
          • (2021)Challenges and Accomplishments of Practicing Formative Assessment: a Case Study of College Biology Instructors’ ClassroomsInternational Journal of Science and Mathematics Education10.1007/s10763-020-10149-8Online publication date: 28-Jan-2021
          • (2020)Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics applicationPLOS ONE10.1371/journal.pone.023397715:6(e0233977)Online publication date: 12-Jun-2020
          • (2020)Learning Analytics and the Measurement of Learning EngagementAdoption of Data Analytics in Higher Education Learning and Teaching10.1007/978-3-030-47392-1_9(159-176)Online publication date: 11-Aug-2020
          • (2020)Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analyticsApplied Cognitive Psychology10.1002/acp.365234:4(890-905)Online publication date: 27-Mar-2020
          • (2019)Learning Feedback Based on Dispositional Learning AnalyticsMachine Learning Paradigms10.1007/978-3-030-13743-4_5(69-89)Online publication date: 17-Mar-2019

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media